Regional Energy Balance Research Articles

Modeling the hydroclimatic effects of local land use and land cover changes on the water budget in the upper Euphrates – Tigris basin

The waters of the Euphrates and Tigris rivers have always been a vital resource in the water-food-energy nexus of the Middle East region. The currently ongoing Southeastern Anatolia Project (GAP) in Turkey aims to increase regional prosperity by optimizing the use of these waters for irrigation and hydropower. Since the beginning of the 1990s, the irrigation schemes and water management infrastructures within the scope of the GAP have caused significant land use and land cover (LULC) change in this semi-arid region. We employed a high resolution regional climate model to simulate the effects of irrigation induced LULC changes on the regional water and energy balances. For this purpose, historical simulations were conducted by using three land cover distributions which reflect the increase in irrigation and water surfaces. Our experiment reveals that water loss through evapotranspiration increases significantly with the areal expansion of irrigation. This increase is driven by the change in partitioning of the available energy at the surface between turbulent heat fluxes. On the one hand, a significant reduction in sensible heat flux causes local cooling by around 0.4 °C and 0.8 °C for the current and future irrigation conditions, respectively. On the other hand, the increase in latent heat flux enhances evapotranspiration and consequently atmospheric water vapor concentration. The moistening of a shallower boundary layer triggers the formation of convective clouds, which increases convective precipitation, most notably during the irrigation months. The enhanced water loss through evapotranspiration has potential to significantly alter the water budget of the GAP region. It seems that the water surplus of the headwaters region may not be enough to meet the water deficit of the GAP region in the future if the planned irrigation schemes are carried out to completion.

BSIS: Blockchain-Based Secure Incentive Scheme for Energy Delivery in Vehicular Energy Network

Vehicular energy network (VEN), as an important part of the Internet of Things for the smart city, can facilitate the renewable energy (RE) transportation over a large geographical area by means of electric vehicles (EVs) through wireless power transfer technology. However, due to the potential security vulnerability in VEN, EV users can be attacked by external or internal adversaries. In addition, owing to the selfishness of EVs, it is a great challenge to optimally schedule the charging/discharging behaviors of EVs to realize regional energy balance in VEN. To tackle the above issues, this paper proposes a blockchain-based secure incentive scheme for energy delivery in VEN. First, a novel permissioned energy blockchain system is introduced in VEN to implement secure energy delivery services for EVs and energy nodes through the use of distributed ledgers and cryptocurrency. Second, a proof of reputation consensus protocol is proposed to efficiently reach consensus in energy blockchain, where the reputation derivation is constructed based on the local trust computing and credibility computing. Third, motivated by the pricing mechanism, an incentive model is developed to stimulate EVs to cooperatively deliver RE to various areas with different electricity loads while maximizing EVs’ utilities. Finally, extensive numerical results are provided, which demonstrate the efficiency of the proposed scheme through the comparison with conventional schemes.

Impact of Climate Changes on the Regional Energy Balances and Energy Exports from Russia

Based on the approaches developed by the National Research University Moscow Power Engineering Institute (NRU MPEI) and the Energy Research Institute (ERI), the results of calculations of total world energy consumption and by groups of countries for the period up to 2050, with allowance for the anticipated climate changes, as well as resource and environmental restrictions, are presented. The estimates of energy consumption in various world regions for the period up to 2050 are based on a historical extrapolation approach to the research of energy consumption dynamics with allowance for the dependence of its optimal level on natural and geographical conditions. Using the SCANER simulation computer system developed by the Energy Research Institute for the study of energy markets, forecast fuel and energy balances for various regions have been made, which are aimed at meeting the national commitments on the Paris Agreement (2015) of the countries participating in the UN Framework Convention on Climate Change (UNFCCC), and international flows of the main energy carriers have been calculated. Estimates of the possible oil and gas export from Russia in the context of various scenarios for the development of the world energy sector have been made. It is shown that the fundamental processes currently taking place in the global energy complex, such as the development of new renewable and nonrenewable energy sources, the control and constraint on greenhouse gas emissions, and the uncertain status of nuclear energy, will contribute to an increase in demand for oil and, especially, natural gas from Russia and facilitate the rise in exports of these energy carriers, predominantly in the Asian direction.

The information system for the regional energy balance formation

A study of the energy efficiency of the fuel and energy complex of the regions has a great attention. For an integrated introduction of trends and patterns in the energy sector, approaches we use based on the analysis of the fuel and energy balance (FEB). The function of FEB is to show the availability and use of energy resources in a certain territory and determination of indices of energy efficiency. The article presents an approach to the formation of fuel and energy balances based on the available statistical information.

Geospatial Monitoring of Land Surface Temperature Effects on Vegetation Dynamics in the Southeastern Region of Bangladesh from 2001 to 2016

Land surface temperature (LST) can significantly alter seasonal vegetation phenology which in turn affects the global and regional energy balance. These are the most important parameters of surface–atmosphere interactions and climate change. Methods for retrieving LSTs from satellite remote-sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on the Earth’s surface. This paper assesses the geospatial patterns of LST using correlations of the seasonally integrated normalized difference vegetation index (SINDVI) in the southeastern region of Bangladesh from 2001 to 2016. Moderate Resolution Imaging Spectroradiometer (MODIS) time series datasets for LST and SINDVI were used for estimations in the study. From 2001 to 2016, the MODIS-based land surface temperature in the southeastern region of Bangladesh was found to have gently increased by 0.2 °C (R2 = 0.030), while the seasonally integrated normalized difference vegetation index also increased by 0.43 (R2 = 0.268). The interannual average LSTs mostly increased across the study areas, except in some coastal plain and tidal floodplain areas of the study. However, the SINDVI increased in the floodplain and coastal plain regions, except for in hilly areas. Physiographically, the study area is a combination of low lying alluvial floodplains, river basin wetlands, tidal floodplains, tertiary hills, terraced lands and coastal plains in nature. The hilly areas are mostly covered by dense forests, with the exception of agricultural areas. The impacts of increased LSTs were inversely correlated for the hilly areas and areas with forest coverage; LSTs were conversely correlated for the floodplain region, and tree cover outside of the forest and agricultural crops. This study will be very helpful for the protection and restoration of the natural environment.

Assessing the Coupled Influences of Clouds on the Atmospheric Energy and Water Cycles in Reanalyses with A-Train Observations

The lack of complete knowledge concerning the complex interactions among clouds, circulation, and climate hinders our ability to simulate the Earth’s climate correctly. This study contributes to a broader understanding of the implications of cloud and precipitation biases on the representation of coupled energy and water exchanges by bringing together a suite of cloud impact parameters (CIPs). These parameters measure the coupled impact of cloud systems on regional energy balance and hydrology by simultaneously capturing the absolute strength of the cloud albedo and greenhouse effects, the relative importance of these two radiative effects, and the efficiency of precipitating clouds to radiatively heat the atmosphere and cool the surface per unit of heating through rain production. Global distribution of these CIPs is derived using satellite observations from CloudSat and used to evaluate energy and water cycle coupling in four reanalysis datasets [both versions of the Modern-Era Retrospective Analysis for Research and Applications (MERRA and MERRA-2); the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim); and the Japanese 55-year Reanalysis (JRA-55)]. The results show that the reanalyses provide a more accurate representation of the three radiation-centric parameters than the radiative efficiencies. Of the four reanalyses, MERRA and ERA-Interim provide the best overall representation of the different cloud processes but can still show significant biases. JRA-55 exhibits some clear deficiencies in many parameters, while MERRA-2 seems to introduce biases that were not evident in MERRA.

Radiative feedbacks of dust in snow over eastern Asia in CAM4-BAM

Abstract. Dust in snow on the Tibetan Plateau (TP) could reduce the visible snow albedo by changing surface optical properties and removing the snow cover through increased snowmelt, which leads to a significant positive radiative forcing and remarkably alters the regional energy balance and the eastern Asian climate system. This study extends our previous investigation in dust–radiation interactions to investigate the dust-in-snow radiative forcing (SRF) and its feedbacks on the regional climate and the dust cycle over eastern Asia through the use of the Community Atmosphere Model version 4 with a Bulk Aerosol Model parameterizations of the dust size distribution (CAM4-BAM). Our results show that SRF increases the eastern Asian dust emissions significantly by 13.7 % in the spring, countering a 7.6 % decrease in the regional emissions by the dust direct radiative forcing (DRF). SRF also remarkably affects the whole dust cycle, including transport and deposition of dust aerosols over eastern Asia. The simulations indicate an increase in dust emissions of 5.1 %, due to the combined effect of DRF and SRF. Further analysis reveals that these results are mainly due to the regional climatic feedbacks induced by SRF over eastern Asia. By reducing the snow albedo over the TP, the dust in snow mainly warms the TP and influences its thermal effects by increasing the surface sensible and latent heat flux, which in turn increases the aridity and westerly winds over northwestern China and affects the regional dust cycle. Additionally, the dust in snow also accelerates the snow-melting process, reduces the snow cover and then expands the eastern Asian dust source region, which results in increasing the regional dust emissions. Hence, a significant feature of SRF on the TP is the creation of a positive feedback loop that affects the dust cycle over eastern Asia.

Estimation of snow accumulation over frozen Arctic lakes using repeat ICESat laser altimetry observations – A case study in northern Alaska

The spatial distribution and temporal dynamics of snow in Arctic regions have a direct impact on the regional energy balance and hydrologic cycle. However, our knowledge of snow cover in Arctic regions is very limited due to the sparseness of in situ measurements. This study presents a new method to derive snow accumulation information for Arctic regions through tracking the surface elevation changes of numerous frozen arctic lakes measured by Ice, Cloud, and land Elevation satellite (ICESat) repeat altimetry observations. The original ICESat elevation product for continental surface was generated by tracking the centroids of the emitted and the returned laser waveforms. This product contains many biased measurements over frozen arctic lake surfaces due to the scattering effects induced by thin clouds and blowing snow. This study derives an operational approach that produces more reliable altimetry observations by converting the elevation measurements from the centroid scheme to the max-amplitude-peak scheme. Time-variable biases exist between the repeat elevation measurements acquired in different ICESat campaigns. The correction of these inter-campaign biases in this study significantly improves the quantification of the surface elevation change, thus enabling more consistent subsequent snow accumulation estimates. Besides snow fall, lake ice growth also contributes to the surface elevation change. We developed a method to measure and remove this contribution from the total lake surface elevation change, which leads to more accurate estimates of snow accumulation on frozen surfaces of 277 lakes in Arctic regions of northern Alaska. The results were validated using in situ snow depth observations from terrestrial stations on the Arctic coastal plain of Alaska. After the correction of the ICESat inter-campaign biases and the removal of contribution by lake surface phase transformation, the snow accumulation derived from the repeat ICESat elevation measurements are highly correlated with in situ snow depth observations with a Pearson's correlation coefficient r of 0.88. In comparison with ground-based measurements, the root mean square error (RMSE) of our snow accumulation estimates is approximately 5 cm. Our method makes it possible to provide much denser snow accumulation information as compared to the existing in situ observations for the Circum-Arctic coastal regions and also the Qinghai-Tibet Plateau where seasonal frozen lakes are abundant.

Support vector regression snow-depth retrieval algorithm using passive microwave remote sensing data

Snow cover is an informative indicator of climate change because it can affect local and regional surface energy and water balance, hydrological processes and climate, and ecosystem function. Passive microwave (PM) remote sensing data have long been used to retrieve snow depth and snow water equivalent with large uncertainties. The objective of this study is to develop snow-depth retrieval algorithm based on support vector regression (SVR) technique using PM remote sensing data and other auxiliary data. Ground-based daily snow depth data from 1223 stations across Eurasian continent were used to construct and validate the snow-depth retrieval algorithm. This SVR snow-depth retrieval algorithm partitioned three snow cover stages, and four land cover types then generated twelve snow-depth models for each phases. A non-linear regression method based on support vector regression (SVR) was used to retrieve snow depth with PM brightness temperatures, location (latitude and longitude), and terrain parameters (elevation) as input data and land cover as auxiliary data. In addition, we compared the performance of the SVR snow-depth retrieval algorithm with four alternative algorithms: the Chang algorithm, the Spectral Polarization Difference (SPD) algorithm, the Artificial/Neural Networks (ANN) and, an algorithm based on linear regression. Comparing results obtained from these five snow-depth retrieval algorithms against the ground-based daily snow depth data, the SVR snow-depth retrieval algorithm performs much superior with reduced uncertainties. We report the results aimed at evaluating the impact of the variation of snow cover stages and land cover types. The preliminary results suggest that the SVR snow-depth algorithm could detect deep snow with high accuracy and decrease the impact of saturation effects. These results suggest that the SVR snow-depth retrieval algorithm integrating PM remote sensing data and other auxiliary data (land cover types, location, terrain, snow cover stage with indirectly considering grain size variation) can be a more efficient and effective algorithm for retrieving snow depth and snow water equivalent over various scales.

Bottleneck of slab thermal efficiency in reheating furnace based on energy apportionment model

To reveal distribution of slab thermal efficiency (STE) and its bottlenecks, a slab region thermal efficiency (SRTE) model and an STE model were established based on the energy apportionment model of a reheating furnace in this paper. The bottleneck index of the slab thermal efficiency (BISTE), which could be used to assess the influence of SRTE on STE in a particular region, was proposed. First, the regional energy balance equation was listed based on reheating furnace region division. Next, the SRTE and STE models were established. Second, the bottleneck of slab thermal efficiency (BSTE) was achieved through a partial correlation analysis (PCA) of billet samples, which were obtained according to the difference between billet loading temperature and its residence time in the reheating furnace. Next, the BISTE was advanced to accurately determine the BSTE. Finally, several suggestions or measures, that could improve SRTE, were proposed. The case study has demonstrated the validation of these models, and the BISTE was 42% (Preheating), 19% (Heating II), 18% (Soaking), 11% (Heating I) and 10% (Preheating & Heating), respectively. Therefore, the preheating zone is the key region used to improve STE.

The Nexus between Sea Ice and Polar Emissions of Marine Biogenic Aerosols

AbstractAccurate estimation of the climate sensitivity requires a better understanding of the nexus between polar marine ecosystem responses to warming, changes in sea ice extent, and emissions of marine biogenic aerosol (MBA). Sea ice brine channels contain very high concentrations of MBA precursors that, once ventilated, have the potential to alter cloud microphysical properties, such as cloud droplet number, and the regional radiative energy balance. In contrast to temperate latitudes, where the pelagic phytoplankton are major sources of MBAs, the seasonal sea ice dynamic plays a key role in determining MBA concentration in both the Arctic and Antarctic. We review the current knowledge of MBA sources and the link between ice melt and emissions of aerosol precursors in the polar oceans. We illustrate the processes by examining decadal-scale time series in various satellite-derived parameters such as aerosol optical depth (AOD), sea ice extent, and phytoplankton biomass in the sea ice zones of both hemispheres. The sharpest gradients in aerosol indicators occur during the spring period of ice melt. In sea ice–covered waters, the peak in AOD occurs well before the annual maximum in biomass in both hemispheres. The results provide strong evidence that suggests seasonal changes in sea ice and ocean biology are key drivers of the polar aerosol cycle. The positive trend in annual-mean Antarctic sea ice extent is now almost one-third of the magnitude of the annual-mean decrease in Arctic sea ice, suggesting the potential for different patterns of aerosol emissions in the future.

Assessing direct and indirect emissions of greenhouse gases in road transportation, taking into account the role of uncertainty in the emissions inventory

Greenhouse gas (GHG) concentration in the atmosphere has increased since the beginning of the industrial era, with dramatic effects on climate change. Transportation is one of the main sources of GHGs, with more than two-thirds of transport-related GHG emissions attributable to road vehicles. Any policy that aims to reduce GHG emissions needs robust measuring methods that guarantee the quality and reliability of primary data and estimates. However, these estimates are subject to uncertainty, both at the stage of compiling accounting tables and at the stage of using this information to formulate a specific policy question.This paper considers how to reduce uncertainty in estimating GHG emissions from road transportation, with specific reference to a regional emissions inventory in Italy. We propose the application of a use-chain model that can tackle uncertainty in measuring GHG emissions by enhancing the quality of the emissions data registry in the inventory. This new metric, which we call emission value at risk (VaR), draws from methodologies and concepts employed in the insurance and financial sectors. Moreover, additional assessments are performed, integrating the inventory data with those available in the regional energy balance and disaggregated sectoral economic dataset. The results show that a sound accounting method enables uncertainty in emission data to be taken into account, thus improving the design of appropriate strategies to reduce GHG emissions.

Dry and Wet Snow Cover Mapping in Mountain Areas Using SAR and Optical Remote Sensing Data

Snow cover in mountain areas is a key factor controlling regional energy balances, hydrological cycle, and water utilization. Optical remote sensing data offer an effective means of mapping snow cover, although their application is limited by solar illumination conditions, conversely, synthetic aperture radar (SAR) offers the ability to measure snow wetness changes in all weather. In this study, a novel method, which can be approached in two steps by using SAR and optical data, has been developed for dry and wet snow cover recognition in mountain areas. First, two ground-based synchronous observations were implemented, respectively, for snow-accumulation period and snow-melt period. Then, the RADARSAT-2 interferometric coherence images and the backscattering coefficient images of the two periods are analyzed, adopting snow-covered and snow-free areas obtained from GF-1 satellite observations as the “ground truth.” A dynamic thresholding algorithm was proposed to identify snow cover by taking the polarization mode, local incidence angle, and underlying surface type into consideration. Finally, 36 polarimetric parameters obtained from Pauli, H/A/α , Freeman, and Yamaguchi decomposition were analyzed; the results indicate that P vol from Pauli, λ3 from H/A/α , and Y vol from Yamaguchi are more applicable to discriminate dry and wet snow. These three factors, combined with training samples from Nagler algorithm and in situ data, were used to build a support vector machine to classify the extracted snow cover to dry and wet snow. The classification results demonstrate that the dry and wet snow cover extraction can achieve an accuracy of 90.3% compared with in situ measurements.

Cloud removing method for daily snow mapping over Central Asia and Xinjiang, China

Central Asia and Xinjiang, China are conjunct areas, located in the hinterland of the Eurasian continent, where the snowfall is an important water resource supplement form. The induced seasonal snow cover is vita factors to the regional energy and water balance, remote sensing plays a key role in the snow mapping filed, while the daily remote sensing products are normally contaminated by the occurrence of cloud, that obviously obstacles the utility of snow cover parameters. In this paper, based on the daily snow product from Moderate Resolution Imaging Spectroradiometer (MODIS A1), a cloud removing method was developed by considering the regional snow distribution characteristics with latitude and altitude dependence respectively. In the end, the daily cloud free products was compared with the same period of eight days MODIS standard product, revealing that the cloud free snow products are reasonable, while could provide higher temporal resolution, and more details over Center Asia and Xinjiang Province.

Investigation of factors affecting intra-annual variability of evapotranspiration and streamflow under different climate conditions

Investigating the factors that affect intra-annual evapotranspiration (ET) and streamflow variability is important to regional hydrological cycles and energy balance research. In this study, ET and streamflow variability (defined as their standard deviations) are attributed to precipitation, potential evapotranspiration (ET0) and total water storage change (TWSC) based on a Budyko-based approach at 282 catchments in China. The results show that the Budyko-based approach satisfactorily simulates the intra-annual ET and streamflow variability (R2 of 0.63–0.84). The dominant contributor to ET variability is ET0 under energy-limited condition (aridity index⩽0.76), whereas the dominant contributor is precipitation under equitant (0.76<aridity index⩽1.35) and water-limited conditions (aridity index⩾1.35). The contribution of ET0 to ET variability decreases with the aridity index, whereas the contribution of precipitation to ET variability increases with the aridity index. However, the dominant contributor to streamflow variability is precipitation under all the three climate conditions, which is unaffected by the aridity index. TWSC enhances ET variability under energy-limited condition and inhibits ET variability under water-limited and equitant conditions. However, TWSC inhibits streamflow variability under all the three climate conditions. In addition, geography and vegetation also influence the contributors to ET and streamflow variability. The effects of geography on the contributors to streamflow variability are larger than that to ET variability. In contrast, the impacts of vegetation on the contributors to ET variability are larger than that to streamflow variability. This study demonstrates that the mechanism of ET variability under different climate conditions is much more complex than that of streamflow variability, suggesting that more attention should be given to ET for water-energy modeling, hydrological predictions and local water management.

Evaluating Regional and Global Hydrological Models against Streamflow and Evapotranspiration Measurements

Abstract Land surface and global hydrological models are often used to characterize global water and energy fluxes and stores and to model their future trajectories. This study evaluates estimates of streamflow and evapotranspiration (ET) obtained with a priori parameterization from a land surface model [CSIRO Atmosphere Biosphere Land Exchange (CABLE)] and a global hydrological model (H08) against a global dataset of streamflow from 644 largely unregulated catchments and ET from 98 flux towers and benchmarks their performance against two lumped conceptual daily rainfall–runoff models [modèle du Génie Rural à 4 paramètres Journalier (GR4J) and a simplified version of the HYDROLOG model (SIMHYD)]. The results show that all four models perform poorly in simulating the monthly and annual runoff values, with the rainfall–runoff models outperforming both CABLE and H08. The model biases in runoff are generally reflected as a complementary opposite bias in ET. All models can generally reproduce the observed seasonal and interannual runoff variability. The correlations between the modeled and observed runoff time series are reasonable, with the rainfall–runoff models performing slightly better than CABLE and H08 at the monthly time scale and all four models performing similarly at the annual time scale. The results suggest that while the land surface and global hydrological models cannot adequately simulate the actual runoff time series and long-term average volumes, they can reasonably simulate the monthly and interannual runoff variability and trends and can therefore be reliably used for broadscale or comparative regional and global water and energy balance assessments and simulations of future trajectories. They can be improved through validating the models or calibrating some of the more sensitive and less physically based parameters.

Green Technology and Sustainable Development of Renewable Energy

Carbon dioxide (CO 2 ) and other greenhouse gases (GHG) emissions, which cause global warming, have become a major worldwide concern with ten global ‘mega’ challenges that are currently impacting the planet in particular climate change, water, energy, and material resource scarcity. Increase in urbanization rate will continue to increase its need for natural resources, building materials, power and electricity, water, etc. that leads to bio-capacity deficit with a sharp increase in generation of ecological footprints. Power-generating plants running on fossil fuels have been identified as the main source of greenhouse gases (GHG). Approximately, 80% of the world primary energy consumption is still dependent on fossil fuels; thus, the substitution by renewable energy sources, in conjunction with other clean energy sources, appears to be the best and necessary alternative. There are many other sources of renewable energy such as solar, wind, and geothermal, but biomass have been receiving a lot of attention lately. Biomass has gained increased attention in the past decade because it does not only provide an effective option for the provision of energy services from a technical point of view, but also based on resources that can be utilized on a sustainable basis all around the globe. Another benefit of biomass utilization is that this resource can be converted to C3-C4 hydrocarbons and/or synthetic gas (H 2 and CO). Statistics shows that the urban areas with industrial, residential and commercial activities are the large energy “deficit zones”. Thus, the transfer of biomass energy sources from the “surplus zones”, which are those surrounding rural / plantation areas to the major urban cities, can be a solution that bring numerous benefits to the regions: (i) Environmental – minimization of CO 2 emissions and other gases of the greenhouse effect (ii) Energy - improvement in the regional energy balance, reinforcement of energy independence, and (iii) Economical – maximization of the utilization of local energy sources and adding the value to the “waste”. There is a great potential for exploitation of local energy sources from wastes but the strength of policy support and stimulation measures are far from sufficient. At this moment, there is lack of studies about the integration of different types of wastes and the application of green technology approach, in addition to proper supply chain analysis and synthesis for waste-to-energy (WTE) system. These missing studies are crucial for solving both energy and environmental problems especially in the urban area. Further innovative and cost effective process needs to be developed to ensure the competitiveness of green industry. Producing biodiesel from non-edible feedstock is one of the solutions to reduce the import volume. Furthermore, public awareness and acceptance on the utilization of green fuels needs to be promoted and increased. Knowledge dissemination on the advantages of the biofuel utilization in the transportation sectors could be achieved through outreach initiatives to young minds and public. Increasing demand of transportation fuel has made utilizing biofuels more attractive since it helps to reduce CO 2 to 78.45% compared to if purely fossil fuel based is utilized. The sector which emits the most CO 2 is the transportation sector. Energy for transportation is projected to be the fastest growing sector during the next five years, expanding at an annual rate of 5.3%. Non-edible resources as fuel are thus important to avoid food vs fuel crisis and reduce dependency on fossil fuel. Furthermore, world population is projected to grow from 6.5 billion in 2005 to nearly 9.2 billion by 2050. To feed a population of more than 9 billion, global food production must be doubled by 2050. Additionally, reliance on a single source of feedstock for biodiesel production has its setback. Thus, diversification of the feedstock towards non-edible materials with a minimal retrofit to existing production facilities will help to overcome the situation. Sustainable development of the energy sector is one of the key factors to maintain economic competitiveness and progress. The initiative of the Green Technology promotes minimization growth of energy consumptions while enhancing economic development. In addition, it will increase national capability, capacity and awareness for innovation in Green Technology. Overall, the policies and incentives on renewable energy are important to promote low carbon economy and society in future. Citation: Yusup, S. (2016). Green Technology and Sustainable Development of Renewable Energy. Trends in Renewable Energy, 2(3), 83-84. DOI: 10.17737/tre.2016.2.3.0024

The Outlook for Low-Grade Fuels in Tomsk Region: Research Experience at Tomsk Polytechnic University

The urgency of the discussed issue is caused by the need to substitute in the regional fuel-energy balances imported energy resources with local low-grade fuels. The main aim of the study is to estimate thermal properties of local fuels in Tomsk region and evaluate its energy use viability. The methods used in the study were based standard GOST 52911-2008, 11022-95 and 6382-2001, by means of a bomb calorimeter ABK-1 and Vario micro cube analyzer. The mineral ash of researched fuels was studied agreeing with GOST 10538-87. The results state the fact that discussed low-grade fuels of Tomsk region in the unprepared form are not able to replace imported coal in regional energy balance, because of the high moisture and ash content values. A promosing direction of a low-temperature fue processing is a catalytic converter, which allows receiving hydrogen-enriched syngas from the initial solid raw.

Increase in Aerosol Black Carbon in the 2000s over Ny-Ålesund in the Summer

Abstract To investigate the concentrations, sources, and temporal variations of atmospheric black carbon (BC) in the summer Arctic, routine ground-level observations of BC by optical absorption were made in the summer from 2005 to 2008 at the Chinese Arctic “Yellow River” Station (78°55′N, 11°56′E) at Ny-Ålesund on the island of Spitsbergen in the Svalbard Archipelago. Methods of the ensemble empirical-mode decomposition analysis and back-trajectory analysis were employed to assess temporal variation embedded in the BC datasets and airmass transport patterns. The 10th-percentile and median values of BC concentrations were 7.2 and 14.6 ng m−3, respectively, and hourly average BC concentrations ranged from 2.5 to 54.6 ng m−3. A gradual increase was found by 4 ng m−3 a−1. This increase was not seen in the Zeppelin Station and it seemed to contrast with the prevalent conception of generally decreasing BC concentration since 1989 in the Arctic. Factors responsible for this increase such as changes in emissions and atmospheric transport were taken into consideration. The result indicated that BC from local emissions was mostly responsible for the observed increase from 2005 to 2008. BC temporal variation in the summer was controlled by the atmospheric circulation, which presented a significant 6–14-day variation and coherent with 1–3- and 2–5-day and longer cycle variation. Although the atmospheric circulation changes from 2005 to 2008, there was not a marked trend in long-range transportation of BC. This study suggested that local emissions might have significant implication for the regional radiative energy balance at Ny-Ålesund.

Evapotranspiration Cycles in a High Latitude Agroecosystem: Potential Warming Role.

As the acreages of agricultural lands increase, changes in surface energetics and evapotranspiration (ET) rates may arise consequently affecting regional climate regimes. The objective of this study was to evaluate summertime ET dynamics and surface energy processes in a subarctic agricultural farm in Interior Alaska. The study includes micrometeorological and hydrological data. Results covering the period from June to September 2012 and 2013 indicated consistent energy fractions: LE/R net (67%), G/R net (6%), H/R net (27%) where LE is latent heat flux, R net is the surface net radiation, G is ground heat flux and H is the sensible heat flux. Additionally actual surface evapotranspiration from potential evaporation was found to be in the range of 59 to 66%. After comparing these rates with those of most prominent high latitude ecosystems it is argued here that if agroecosystem in high latitudes become an emerging feature in the land-use, the regional surface energy balance will significantly shift in comparison to existing Arctic natural ecosystems.